Method of providing retirement and estate planning for property owners of a residential net lease

Abstract

The present disclosure provides a method for automating a residential net lease management tool that provides risk-return projections associated with net lease terms and financial planning data. Based on market data, a reserve module generates net lease parameters, identifies properties within these parameters, determines costs, generates net lease terms, obtains owner approval, considers financial planning data like retirement allocation or estate planning, uses machine learning models in the financial planning module to create risk-return projections, and displays projections for user interaction.

What is claimed is: 
     
         1 . A computer-implemented method of automating a residential net lease management tool that provides risk-return projections associated with net lease terms and financial planning data, comprising:
 receiving, over an expense network, market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at least one third-party application;   initiating, by a net lease module, a reserve module;   generating, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region;   initiating, by the net lease module, an owner module;   identifying, by the owner module, properties that fall within the net lease parameters generated by the reserve module;   initiating, by the net lease module, a manage module;   determining, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data;   generating, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on first inputs including the fixed costs and variable costs determined the manage module, wherein first weights are assigned to each first input;   receiving an approval from a property owner of the generated set of net lease terms;   determining that the property owner is associated with financial planning data including at least one of retirement allocation data or estate planning data;   generating, by a first machine-learning model of a financial planning module, risk-return projections based on second inputs including the net lease terms and the financial planning data, wherein second weights are assigned to each second input; and   causing to display the risk-return projections associated with the net lease terms and the financial planning data.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 using a second machine-learning model to output the set of net lease terms, and wherein the machine-learning model determines the first weights based on training data including past net lease terms associated with the identified properties.   
     
     
         3 . The computer-implemented method of  claim 1 , further comprising:
 performing one or more simulations for comparable net lease terms and comparable financial planning data; and   based on the performed simulations, causing to presenting one or more options of changes to the approved net lease terms and the financial planning data based on better risk-return projections for the comparable net lease terms and the comparable financial planning data.   
     
     
         4 . The computer-implemented method of  claim 1 , further comprising:
 generating, using a recommendation engine, one or more recommendations for editing the estate planning data based on the risk-return projections;   receiving a selection of approving one of the recommendations; and   editing the estate planning data based on the approved recommendation.   
     
     
         5 . The computer-implemented method of  claim 4 , wherein the first machine-learning model determines the second weights based on training data including past risk-return projections associated with past net lease terms and past financial planning data including at least one of past retirement allocation data or past estate planning data. 
     
     
         6 . The computer-implemented method of  claim 4 , further comprising:
 using a recommendation machine-learning model to output the one or more recommendations for editing the estate planning data, and wherein the recommendation machine-learning model includes a risk prediction model and a return estimation model;   receiving, by the risk prediction model, the estate planning data as an input and outputs a risk score for each section;   receiving, by the return estimation model, at least part of the financial planning data and the risk-return projections as input and estimates potential returns on investment for each section; and   combining the risk score for each section and the estimated potential returns on investment for each section to generate recommendations for edits for mitigating risks while maximizing potential returns.   
     
     
         7 . The computer-implemented method of  claim 6 , further comprising:
 receiving a selection to approve one of the recommended edits; and   editing the estate planning data based on the selection.   
     
     
         8 . The computer-implemented method of  claim 7 , further comprising:
 retraining the recommendation machine-learning model with new extracted historical data including the selection of the approved recommended edit.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 recording, by an accounting module in a reserve database associated with a single reserve fund, a first accounting for a first amount funded by one or more investors that are not the respective owners; and   recording, by the accounting module in the reserve database associated with the single reserve fund, a second accounting for a second amount remunerated to the investors based on determined profit margins over term of lease and the net lease terms stored at the lease database; and   sending, based upon the accountings of the reserve database over the communication network, an instruction to trigger a transfer to the single reserve fund.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein the market data includes at least one of starting market rent, market growth rate, inflation rate, vacancy rate, rent collectability rate, home price appreciation, operating expenses, local taxes, insurance rates, management amounts, maintenance budget, homeowner's association amounts, cost of utilities, or asset management amounts. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the inputs include at least one of average rent in one or more regions associated with the identified properties, square footage of the respective property, market growth rate, inflation rate, vacancy rate, rent collectability rate, home price appreciation, or operating expenses. 
     
     
         12 . A system for automating a residential net lease management tool for providing risk-return projections associated with net lease terms and financial planning data, comprising:
 a storage configured to store instructions;   a net lease module that controls a reserve module, an owner module, a manage module, and an enhancement module;   the reserve module that generates a plurality of net lease parameters for different regions;   the owner module that identifies replacement properties that fall within a particular net lease parameter;   the manage module that determines fixed costs and variable costs;   the enhancement module that generates stress scenarios;   a financial planning module that generates risk-return projections based on selected net lease terms and selected financial planning data; and   one or more processors configured to execute the instructions and cause the one or more processors to:
 receiving, over an expense network, market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at least one third-party application; 
 initiating, by the net lease module, the reserve module; 
 generating, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region; 
 initiating, by the net lease module, the owner module; 
 identifying, by the owner module, properties that fall within the net lease parameters generated by the reserve module; 
 initiating, by the net lease module, the manage module; 
 determining, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data; 
 generating, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on inputs including the fixed costs and variable costs determined the manage module, wherein weights are assigned to each input; 
 receiving an approval from a property owner of the generated set of net lease terms; 
 determining that the property owner is associated with financial planning data including at least one of retirement allocation data or estate planning data; 
 generating, by a first machine-learning model of the financial planning module, risk-return projections based on second inputs including the net lease terms and the financial planning data, wherein second weights are assigned to each second input; and 
 causing to display the risk-return projections associated with the net lease terms and the financial planning data. 
   
     
     
         13 . The system of  claim 12 , wherein the one or more processors are configured to execute the instructions and cause the one or more processors to:
 using a second machine-learning model to output the set of net lease terms, and wherein the machine-learning model determines the weights based on training data including past net lease terms associated with the one or more regions.   
     
     
         14 . The system of  claim 12 , wherein the one or more processors are configured to execute the instructions and cause the one or more processors to:
 perform one or more simulations for comparable net lease terms and comparable financial planning data; and   based on the performed simulations, cause to presenting one or more options of changes to the approved net lease terms and the financial planning data based on better risk-return projections for the comparable net lease terms and the comparable financial planning data.   
     
     
         15 . The system of  claim 12 , wherein the one or more processors are configured to execute the instructions and cause the one or more processors to:
 generate, using a recommendation engine, one or more recommendations for editing the estate planning data based on the risk-return projections;   receive a selection of approving one of the recommendations; and   edit the estate planning data based on the approved recommendation.   
     
     
         16 . The system of  claim 15 , wherein the first machine-learning model determines the second weights based on training data including past risk-return projections associated with past net lease terms and past financial planning data including at least one of past retirement allocation data or past estate planning data. 
     
     
         17 . The system of  claim 15 , wherein the processor is configured to execute the instructions and cause the one or more processors to:
 using a recommendation machine-learning model to output the one or more recommendations for editing the estate planning data, and wherein the recommendation machine-learning model includes a risk prediction model and a return estimation model;   receiving, by the risk prediction model, the estate planning data as an input and outputs a risk score for each section;   receiving, by the return estimation model, at least part of the financial planning data and the risk-return projections as input and estimates potential returns on investment for each section; and   combining the risk score for each section and the estimated potential returns on investment for each section to generate recommendations for edits for mitigating risks while maximizing potential returns.   
     
     
         18 . The system of  claim 17 , wherein the processor is configured to execute the instructions and cause the one or more processors to:
 receiving a selection to approve one of the recommended edits; and   editing the estate planning data based on the selection.   
     
     
         19 . A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to:
 receive, over an expense network, market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at least one third-party application;   initiate, by a net lease module, a reserve module;   generate, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region;   initiate, by the net lease module, an owner module;   identify, by the owner module, properties that fall within the net lease parameters generated by the reserve module;   initiate, by the net lease module, a manage module;   determine, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data;   generate, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on inputs including the fixed costs and variable costs determined the manage module, wherein weights are assigned to each input;   receive an approval from a property owner of the generated set of net lease terms;   determining that the property owner is associated with financial planning data including at least one of retirement allocation data or estate planning data;   generate, by a first machine-learning model of a financial planning module, risk-return projections based on second inputs including the net lease terms and the financial planning data, wherein second weights are assigned to each second input; and   cause to display the risk-return projections associated with the net lease terms and the financial planning data.   
     
     
         20 . The non-transitory computer readable medium of  claim 19 , wherein the instructions further cause the computing system to:
 use a second machine-learning model to output the set of net lease terms, and wherein the machine-learning model determines the first weights based on training data including past net lease terms associated with the identified properties.